"Experimaestro is a computer science experiment manager"
Project description
Experimaestro helps in designing and managing complex workflows. It allows for the definition of tasks and their dependencies, ensuring that each step in a workflow is executed in the correct order. Some key aspects of Experimaestro are:
- Task Automation: The tool automates repetitive tasks, making it easier to run large-scale experiments. It's particularly useful in scenarios where experiments need to be repeated with different parameters or datasets.
- Resource Management: It efficiently manages computational resources, which is critical when dealing with data-intensive tasks or when running multiple experiments in parallel.
- Extensibility: Experimaestro is designed to be flexible and extensible, allowing users to integrate it with various programming languages and tools commonly used in data science and research.
- Reproducibility: By keeping a detailed record of experiments, including parameters and environments, it aids in ensuring the reproducibility of scientific experiments, which is a fundamental requirement in research.
- User Interface: While primarily a back-end tool, Experimaestro also offers a user interface to help in managing and visualizing workflows.
The full documentation can be read by going to the following URL: https://experimaestro-python.readthedocs.io
Install
With pip
You can then install the package using pip install experimaestro
Develop
Checkout the git directory, then
pip install -e .
Example
This very simple example shows how to submit two tasks that concatenate two strings. Under the curtain,
- A directory is created for each task (in
workdir/jobs/helloworld.add/HASHID) based on a unique ID computed from the parameters - Two processes for
Sayare launched (there are no dependencies, so they will be run in parallel) - A tag
yis created for the main task
# --- Task and types definitions
import logging
logging.basicConfig(level=logging.DEBUG)
from pathlib import Path
from experimaestro import Task, Param, experiment, progress
import click
import time
import os
from typing import List
# --- Just to be able to monitor the tasks
def slowdown(sleeptime: int, N: int):
logging.info("Sleeping %ds after each step", sleeptime)
for i in range(N):
time.sleep(sleeptime)
progress((i+1)/N)
# --- Define the tasks
class Say(Task):
word: Param[str]
sleeptime: Param[float]
def execute(self):
slowdown(self.sleeptime, len(self.word))
print(self.word.upper(),)
class Concat(Task):
strings: Param[List[Say]]
sleeptime: Param[float]
def execute(self):
says = []
slowdown(self.sleeptime, len(self.strings))
for string in self.strings:
with open(string.__xpm_stdout__) as fp:
says.append(fp.read().strip())
print(" ".join(says))
# --- Defines the experiment
@click.option("--port", type=int, default=12345, help="Port for monitoring")
@click.option("--sleeptime", type=float, default=2, help="Sleep time")
@click.argument("workdir", type=Path)
@click.command()
def cli(port, workdir, sleeptime):
"""Runs an experiment"""
# Sets the working directory and the name of the xp
with experiment(workdir, "helloworld", port=port) as xp:
# Submit the tasks
hello = Say(word="hello", sleeptime=sleeptime).submit()
world = Say(word="world", sleeptime=sleeptime).submit()
# Concat will depend on the two first tasks
Concat(strings=[hello, world], sleeptime=sleeptime).tag("y", 1).submit()
if __name__ == "__main__":
cli()
which can be launched with python test.py /tmp/helloworld-workdir
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